Aiming at the problem that the current anti-drone system can not effectively suppress the drone, a new compound anti-drone method is constructed by using multiple types of intercepting equipment. The traditional multi-objective optimization algorithm cannot solve the dynamic task allocation problem, this paper proposes a task assignment model of multi-type interception equipment compound anti-drone based on a deep Q network (DQN). The DQN module makes initial decisions on task allocation issues. In order to improve the convergence speed and learning efficiency of the algorithm, this method does not use the state of the next time to predict the Q value, but uses the state of the current time to predict the Q value, while eliminating the influence of over estimation of $ Q $ value in the training process. After that, an evolutionary algorithm is used to optimize the decision-making results and output multiple interception schemes. The simulation environment of task assignment of the anti-drone system is constructed by taking the open area around a domestic airport runway as the protection object. The simulation results verify the effectiveness of this method. At the same time, compared with the DQN and Double DQN methods, the improved DQN algorithm training agent performance is more accurate, and the convergence of the algorithm and the performance of the solution are more excellent. The proposed method provides new ideas for the anti-drones problem.